View scrape.py
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#!/usr/bin/env python3 | |
import requests | |
from bs4 import BeautifulSoup | |
url = "https://archive.org/stream/ahandbooksocial00blisgoog/ahandbooksocial00blisgoog_djvu.txt" | |
response = requests.get(url) | |
soup = BeautifulSoup(response.text, "html.parser") | |
pre_selector = "#maincontent > div > pre" |
View server.rb
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require "webrick" | |
server = WEBrick::HTTPServer.new(:Port => 8000, :DocumentRoot => Dir.pwd) | |
trap("INT") { server.stop } | |
server.start |
View bubble_sort.rb
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def bubble_sort(arr) | |
for i in 0...arr.length | |
sorted = true | |
for k in 0...(arr.length - i - 1) | |
if arr[k] > arr[k + 1] | |
arr[k], arr[k + 1] = arr[k + 1], arr[k] | |
sorted = false | |
end | |
end |
View twitterusers.py
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#!/usr/bin/python3 | |
import tweepy | |
import csv | |
consumer_key = "" | |
consumer_secret = "" | |
access_token = "" | |
access_token_secret = "" |
View metro.R
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library(tidycensus) | |
library(tidyverse) | |
# If not set, un-comment below and install your Census API key (https://api.census.gov/data/key_signup.html) | |
# census_api_key("YOUR KEY HERE", install = TRUE) | |
get_acs(geography = "metropolitan statistical area/micropolitan statistical area", | |
variables = "DP03_0021PE", | |
summary_var = "B01003_001", | |
survey = "acs1", |
View census_cleanup.R
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library(tidyverse) | |
library(tidycensus) | |
# My recommendation is to use the tidycensus library to make getting this data | |
# easier than reading in the data from the Census website. | |
# | |
# Before you can begin, you'll need to get an API key from the Census Bureau. | |
# You can acquire one here: | |
# | |
# Once you have the API key, run the following in RStudio: |
View messy.R
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library(charlatan) | |
library(salty) | |
library(magrittr) | |
library(readr) | |
messydata <- ch_generate('name','job','phone_number', n = 200) | |
messydata <- messydata %>% | |
mutate(job = salt_capitalization(job)) %>% | |
mutate(phone_number = salt_na(phone_number)) %>% |
View frequency_to_list.R
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library(tidyverse) | |
library(readxl) | |
data <- readxl::read_xlsx("data.xlsx") | |
reshaped <- data %>% gather(word, freq, 2:21) | |
reshaped <- reshaped %>% drop_na() | |
cleaned <- reshaped %>% | |
uncount(freq) |
View geofilter.R
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library(tidyverse) | |
library(maps) | |
library(mapdata) | |
data <- read_csv("~/Desktop/nplsuperfund.csv") | |
names(data) <- c("lat","lon","date") | |
# Filter down to USA extent to remove extraneous points | |
tidy <- data %>% | |
filter(lat < -67, lat > -125) %>% |
View hex_logo.R
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library(hexSticker) | |
library(tidyverse) | |
library(tidycensus) | |
library(sf) | |
library(viridis) | |
options(tigris_use_cache = TRUE) | |
nebraska_raw <- get_acs(state = "NE", | |
geography = "tract", |
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